Probability. Ms. Weinstein Probability & Statistics


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1 Probability Ms. Weinstein Probability & Statistics
2 Definitions Sample Space The sample space, S, of a random phenomenon is the set of all possible outcomes. Event An event is a set of outcomes of a random phenomenon; that is, a subset of the sample space.
3 Definitions Trial A trial is a single occurrence of the random phenomenon. A single trial can result in any one of the possible outcomes in the sample space.
4 Example 1 If the random phenomenon is drawing a card at random out of a standard deck of cards, then the sample space is the set of individual cards in the deck: S = {A, 2,..., Q, K, A, 2,..., Q, K, A, 2,..., Q, K, A, 2,..., Q, K } There are 52 possible outcomes in this sample space.
5 Example 1 (cont d) If an event, call it A, is drawing a card at random and getting a queen, then the event is: A = {Q, Q, Q, Q } There are 4 possible outcomes in this event. A trial would be drawing one card at random from the deck one time.
6 Example 2 If the random phenomenon is tossing a fair coin twice, then the sample space is the set of all possible outcomes of the coin toss: S = { HH, HT, TH, TT } There are 4 possible outcomes in this sample space.
7 Example 2 (cont d) If an event, call it B, is tossing a fair coin twice and getting at least one Tails, then the event is: B = { HT, TH, TT } There are 3 possible outcomes in this event. A trial would be tossing a single coin two times.
8 Notation Notation Read As Means P(A) P of A The probability that event A will occur on any given trial of the random phenomenon
9 Basic Probability Rules Rule # 1: 0 P(A) 1 The probability of any event A that may occur is a number between 0 and 1. 0 < P(A) < 1 An event with P(A) = 0 never occurs. An event with P(A) = 1 always occurs.
10 Basic Probability Rules Rule # 2: P(S) = 1 The collection S of all possible outcomes has probability 1. In other words, every trial must result in one of the possible outcomes.
11 Equally Likely Outcomes If a random phenomenon has k possible outcomes, all equally likely, then each individual outcome has probability 1/k. P(A) = count of outcomes in A count of outcomes in S = count of outcomes in A k
12 Example 1. If event A is drawing a card at random from a standard deck of cards and getting a queen, then: P(A)= Count of outcomes in A Count of outcomes in S
13 Example 2. If event B is tossing a fair coin twice and getting at least one tails, then: P(B) = Count of outcomes in B Count of outcomes in S 3 4
14 Practice 1. You roll a blue and red die at the same time. 1. List the sample space. 2. List the event that you get doubles. 3. Find the following probabilities: a. not getting doubles. b. getting a 5 on the blue die. c. getting a 5 on at least one die.
15 Definitions Complement If A is any event, then the event that A does NOT occur is call the complement of A.
16 Notation Notation Read As Means A or A c or A A complement The event that A does not occur.
17 Complement Rule The probability that the complement of an event occurs is equal to one minus the probability that the event does occur. P(A ) = 1 P(A)
18 Example 1. If event A is drawing a card at random from a standard deck of cards and getting a queen, then the probability of not drawing a queen is: P( A') 1 P( A)
19 Example 2. If event B is tossing a fair coin twice and getting at least one tails, then the probability of not getting a tails on either toss is: P( B') P( B) 3 4
20 Definitions Disjoint Events Events that have no common outcomes are called disjoint events. Union of Events ( OR ) The union of two events is the combined set of outcomes in those events. If the same outcome is in both events, it is only listed once in the union set.
21 Notation Notation Read As Means A union B A B or A or B The event that either A or B occurs (note: both A and B could occur)
22 Addition Rule for Disjoint Events If events A and B are disjoint, then P(A or B) = P(A) + P(B) Set notation: P(A B) = P(A) + P(B)
23 Practice 2. You roll a die and toss a coin. 1. List the sample space. 2. Let A be the event that you get an even number on the die and tails on the coin. Let B be the event that you get heads on the coin. List A and B. 3. a. Find P(A), P(B) and P(A or B) b. Are events A and B disjoint?
24 Definitions Intersection of Events ( AND ) The intersection of two events is the set of outcomes that exist in both of those events. Note: If events are disjoint, then their intersection is an empty set.
25 Notation Notation Read As Means A intersect B A B or A and B The event that both A and B occur
26 General Addition Rule P(A or B) = P(A) + P(B) P(A and B) Set notation: P(A B) = P(A) + P(B) P(A B)
27 Practice 3. You roll a die and toss a coin. 1. Let A be the event that you get an even number on the die. Let B be the event that you get tails on the coin. List A and B. 2. a. Find P(A), P(B), P(A or B), P(A and B) b. Are events A and B disjoint?
28 Representations Venn Diagram Tree Diagram TwoWay Table Probability Distribution
29 Venn Diagrams Start with the overlap areas. The total for each circle is the sum of all the parts in that circle. The sum of all parts together must equal 1. Useful for computing probabilities of events that are not disjoint.
30 Example: P(A) =.3, P(B) =.5, P(A B) =.1 Complete the Venn Diagram to illustrate these probabilities. A B
31 Tree Diagrams Each set of branches from one starting point must equal 1. Multiply the probabilities along each branch to get the probability of all the events on that branch occurring. The sum of all the final probabilities must equal 1. Useful for computing probabilities of independent observations.
32 Example: What is the probability that you will roll a die three times and get at least two threes? Draw a tree diagram to illustrate this. Begin 1/6 5/6 3 Not 3 1/6 5/6 1/6 5/6 3 Not 3 3 Not 3 1/6 5/6 1/6 5/6 1/6 5/6 1/6 5/6 3 Not 3 3 Not 3 3 Not 3 3 Not 3 1/216 5/216 5/216 25/216 5/216 25/216 25/ /216
33 TwoWay Table Twoway tables have two categories: one for the rows, and one for the columns. The sum of the row probabilities is 1. The sum of the column probabilities is 1. Useful for computing conditional probabilities.
34 Example: You are testing a new virusprotection software program. You find that 4% of the time, the software detects a virus when there is no virus. It misses a virus that does exist about 2% of the time. If a virus really exists 5% of the times, complete the 2way table to illustrate this. Virus Exists No Virus Exists Row Totals Virus Detected Not Detected Column Totals
35 Probability Distribution List all the possible outcomes. List the probability of each outcome. The sum of all the probabilities must = 1. Useful for computing probabilities of events that are not equally likely.
36 Example: You roll two dice and add the values. Complete a probability distribution for the sum of the two dice. Step 1: Write out the possible outcomes. Step 2: Calculate the probability of each outcome. Step 3: Summarize the probability distribution.
37 Example: First Die Value Second Die Value Sums
38 Example: You roll two dice and add the values. Complete a probability distribution for the sum of the two dice. Possible Sums Probability of Each Sum
39 Practice 4. A couple has three children. Given that the probability of any child born a boy is ½, a. Draw a tree diagram to illustrate the possible genders of the three children, in order. b. What is the probability that the couple has exactly two girls? c. Complete a probability distribution for the number of girls the couple could have.
40 Definitions Conditional Probability Conditional probability is the probability of an event given that you know another event has occurred.
41 Notation Notation Read As Means P(A B) P of A given B The probability that event A occurs, given that event B has occurred.
42 Conditional Probability The probability of event A occurring, given that event B has occurred is: P(A B) = P(A and B) P(B) Given goes in the denominator! Note: P(B) must not equal 0.
43 Example 1. Draw a card at random from a standard deck. Event A is you draw a queen. Event B is you draw a red card. What is the probability that you draw a queen, given that the card is red? P( A) P( A 4 52 B), P( B) P( A B) P( B) 26, P( A 2 52 B)
44 General Multiplication Rule The probability of two events both occurring is the probability of the first times the probability of the second, given that the first has occurred: P(A and B) = P(A) P(B A) as well as P(A and B) = P(B) P(A B)
45 Definitions Independent Events Two events are said to be independent if knowing the outcome of one event does not change the probability of the other event occurring. That is, P(B A) = P(B) and P(A B) = P(A)
46 Multiplication Rule for Independent Events The probability of two independent events both occurring is the product of their individual probabilities: P(A and B) = P(A) P(B) Note: If this multiplication rule holds, then two events are independent.
47 Practice 5. You draw one card from a standard deck. Event A is the card is a heart. Event B is the card is face card. 1. Are these events independent? How can you know? 2. State an Event in this sample space that would NOT be independent from Event A.
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